Pattern recognition is fundamental to every endeavor in which humans are involved. Decisions are constantly being made based upon the appearance of a pattern, and its comparison to a known references. Typical areas where pattern recognition has been employed include: speech recognition, automated inspection systems and character recognition.
With the advent of the digital computer and the associated advances in microelectronics, storing, accessing and processing large amounts of data became much easier, and hence the use of automated pattern recognition systems has grown. However, the power of these systems has not been fully realized due to the problems with their architecture and associated processing techniques. In particular, a problem which has plagued these systems is their limited operational speed, i.e., these systems are limited in the number of comparisons they can perform per second.
Consider, for example, a digital based pattern recognition system such as that disclosed in U.S. Pat. No. 5,161,204, entitled Apparatus for Generating a Feature Matrix Based on Normalized Out-Class and In-Class Variation Matrices. In this system a feature vector is formed from a digitized unknown pattern, and the feature vector is processed digitally in a neural network to identify the unknown pattern. A problem with this approach is the time that it takes to identify the unknown pattern, primarily due to the processing demands associated with performing a Fourier transformation in a digital system. Special purpose integrated circuits can be used to perform the Fourier transform digitally, but these are expensive and still relatively slow. In addition, neural network pattern recognition systems can not perform "faces in the crowd" identification.
Pattern recognition systems have also employed optical correlators to compare the known and unknown patterns. However, the problems with optical correlators include their complexity, their inflexibility to system changes (i.e., optical components can not easily be changed since the lenses would have to be reground) and their reduced accuracy in comparison to digital systems (assuming of course that the quantization error in the digital system is quite small). In addition, although the actual correlation is very fast once the known and unknown images are presented to the correlator, the speed of the system is constrained by how quickly images of the known and unknown pattern can be exhibited as real images (e.g., on an photographic plate or LCD). The following references disclose pattern recognition systems which utilize optical correlators and are representative of systems plagued by the problems discussed above: 1) "Hybrid Pattern Recognition by Features Extracted from Object Patterns and Fraunhofer Diffraction Pattern" by Takumi Minemoto and Junzo Narano, published in Applied Optics, Vol. 24, No. 18, pg. 2914-2920, Sep. 15, 1995; 2) "Optical Image Processing by an Atomic Vapor", by Ivan Biaggo et al., Nature, Vol. 371, pg. 318-320, Sep. 22, 1994; and 3) U.S. Pat. No. 5,274,716 entitled "Optical Pattern Recognition Apparatus", by Yasuyuki Mitsuoka et al.
Therefore, there is a need in the field of pattern recognition for a fast pattern recognizer to overcome the problems set forth above.